The present invention relates to zero knowledge proofs, and in particular, to zero knowledge proofs for arbitrary predicates of private data.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
The concepts of zero knowledge proofs may lend themselves to applications in the area of power distribution systems. The reading of electricity meters as well as other aspects of power distribution systems are being automated by the increasing use of smart grid technology in power distribution systems. Smart grids include communication links that allow service providers to communicate with various devices that are involved with electricity generation, distribution, and consumption. For example, smart grids allow service providers to communicate with smart meters that are located at consumer locations. Smart meters are replacements of traditional electricity meters. Smart meters and smart grids allow consumers' electricity consumption to be monitored remotely by the service provider, for example, via communications transmitted between the service provider and the smart meters. Thereby, smart meters and smart grids alleviate, at least in part, the historical need for humans to travel to traditional electricity meters to read the traditional electricity meters.
Smart grids also allow service providers to remotely communicate with power generation plants that generate the electricity that the consumers eventually consume. For example, service providers can remotely monitor the amount of electricity that power generation plants are producing and are projected to produce.
Service providers can use information collected from both smart meters and power generation plants to determine whether the electricity that is consumed exceeds, matches, or is falling behind the electricity that is produced by the power generation plants. Using the collected information, service providers can make a variety of timely informed decisions to improve electricity distribution between power generation plants and consumers so that electricity can be distributed both reliability and efficiently.
One technique that service providers can use with smart grids to improve electricity distribution between power generation plants and consumers is known as “demand response”. Demand response allows for the issuance of demands and the issuance of responses that are responsive to the demands. For example, demand response allows for the issuance of a demand by a service provider to a customer and the issuance of a response by the customer to the service provider. Demands can be issued by service providers to consumers in an attempt to influence their electricity consumption.
To help ensure that consumers respond positively to meet demands, service providers may offer bonuses to consumers to encourage the consumers to meet the demands. The bonuses are provided to consumers who actually meet the demand. An example demand response scenario may include a service provider issuing demands (e.g., via a smart gird) to consumers to reduce electricity consumption for a given time period, and issuing bonus offers to those consumers whose electricity consumption meets the reduced electricity consumption. For example, the demand may be issued to the consumers to reduce electricity usage by 10% (e.g., a consumption target) for a day as compared to a previous day. More specifically, demands may be issued to the consumers to consume 10% less electricity in hourly time-slots between 7 PM and 10 PM as compared to the same time slots in the previous day. The service provider may offer bonuses for monetary reductions in an electricity bill if the demand is met.
Eventually, the service provider will have to verify whether the consumer has met the demand before the bonuses are issued. Verification of demands may be accomplished by collecting high-resolution smart-meter data from smart meters via the smart grid. The collection of such high-resolution smart-meter data from smart meters may, however, exposes consumers' private information to the service provider. More specifically, consumers' activities in their homes and businesses can be inferred from high-resolution smart-meter data based on known profiles of electricity usage. For example, the profiles of consumers' electricity consumption may reveal activities, such as watching television, running a dishwasher, sleeping, cooking, operating machinery, etc.
Many consumers want to keep their activities in their homes, businesses, and the like private. Therefore, the consumers do not want their high-resolution smart-meter data revealed to service providers so that they can preserve their privacy. To support the consumers' demands for privacy, service providers may attempt to learn whether a demand has been met without collecting the consumers' high-resolution smart-meter data. Various techniques exist for service providers to determine whether consumers meet a demand without exposing the consumers' high-resolution smart-meter data to the service provider. However, these techniques often are not efficient and/or are not strongly secure and may allow high-resolution smart-meter data to be exposed to the service providers.